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PointGuard: Provably Robust 3D Point Cloud Classification

PointGuard: Provably Robust 3D Point Cloud Classification

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Liu, Hongbin Jia, Jinyuan Gong, Neil Zhenqiang Duke Univ Durham NC 27708 USA

ISBN: (纸本)9781665445092

3D point cloud classification has many safety-critical applications such as autonomous driving and robotic grasping. However, several studies showed that it is vulnerable to adversarial attacks. In particular, an attacker can make a classifier predict an incorrect label for a 3D point cloud via carefully modifying, adding, and/or deleting a small number of its points. Randomized smoothing is state-of-the-art technique to build certifiably robust 2D image classifiers. However, when applied to 3D point cloud classification, randomized smoothing can only certify robustness against adversarially modified points. In this work, we propose PointGuard, the first defense that has provable robustness guarantees against adversarially modified, added, and/or deleted points. Specifically, given a 3D point cloud and an arbitrary point cloud classifier, our PointGuard first creates multiple subsampled point clouds, each of which contains a random subset of the points in the original point cloud;then our PointGuard predicts the label of the original point cloud as the majority vote among the labels of the subsampled point clouds predicted by the point cloud classifier. Our first major theoretical contribution is that we show PointGuard provably predicts the same label for a 3D point cloud when the number of adversarially modified, added, and/or deleted points is bounded. Our second major theoretical contribution is that we prove the tightness of our derived bound when no assumptions on the point cloud classifier are made. Moreover;we design an efficient algorithm to compute our certified robustness guarantees. We also empirically evaluate PointGuard on ModelNet40 and ScanNet benchmark datasets.

关键词： computer vision Three-dimensional displays Smoothing methods Computational modeling Grasping Benchmark testing Prediction algorithms

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Florence-2: Advancing a Unified Representation for a Variety of vision Tasks

Florence-2: Advancing a Unified Representation for a Variety...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Bin Xiao Haiping Wu Weijian Xu Xiyang Dai Houdong Hu Yumao Lu Michael Zeng Ce Liu Lu Yuan Microsoft

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce Florence-2, a novel vision foundation model with a unified, prompt-based representation for various computer vision and vision-language tasks. While existing large vision models excel in transfer learning, they struggle to perform diverse tasks with simple instructions, a capability that implies handling the complexity of various spatial hierarchy and semantic granularity. Florence-2 was designed to take text-prompt as task instructions and generate desirable results in text forms, whether it be captioning, object detection, grounding or segmentation. This multi-task learning setup demands large-scale, high-quality annotated data. To this end, we co-developed FLD-5B that consists of 5.4 billion comprehensive visual annotations on 126 million images, using an iterative strategy of automated image annotation and model refinement. We adopted a sequence-to-sequence structure to train Florence-2 to perform versatile and comprehensive vision tasks. Extensive evaluations on numerous tasks demonstrated Florence-2 to be a strong vision foundation model contender with un-precedented zero-shot and fine-tuning capabilities.

关键词： Visualization computer vision Grounding Annotations Semantics Transfer learning Object detection

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Interventional Video Grounding with Dual Contrastive Learning

Interventional Video Grounding with Dual Contrastive Learnin...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Nan, Guoshun Qiao, Rui Xiao, Yao Liu, Jun Leng, Sicong Zhang, Hao Lu, Wei Singapore Univ Technol & Design StatNLP Res Grp Singapore Singapore Shanghai Jiao Tong Univ Shanghai Peoples R China Singapore Univ Technol & Design Informat Syst Technol & Design Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore ASTAR Inst High Performance Comp Singapore Singapore

ISBN: (纸本)9781665445092

Video grounding aims to localize a moment from an untrimmed video for a given textual query. Existing approaches focus more on the alignment of visual and language stimuli with various likelihood-based matching or regression strategies, i.e., P(Y vertical bar X). Consequently, these models may suffer from spurious correlations between the language and video features due to the selection bias of the dataset. 1) To uncover the causality behind the model and data, we first propose a novel paradigm from the perspective of the causal inference, i.e., interventional video grounding (IVG) that leverages backdoor adjustment to deconfound the selection bias based on structured causal model (SCM) and do-calculus P(Y vertical bar do(X)). Then, we present a simple yet effective method to approximate the unobserved confounder as it cannot be directly sampled from the dataset. 2) Meanwhile, we introduce a dual contrastive learning approach (DCL) to better align the text and video by maximizing the mutual information (MI) between query and video clips, and the MI between start/end frames of a target moment and the others within a video to learn more informative visual representations. Experiments on three standard benchmarks show the effectiveness of our approaches.

关键词： Visualization Correlation Grounding Benchmark testing Knowledge discovery Data models pattern recognition

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Geometry Transfer for Stylizing Radiance Fields

Geometry Transfer for Stylizing Radiance Fields

引用

conference on computer vision and pattern recognition (cvpr)

作者： Hyunyoung Jung Seonghyeon Nam Nikolaos Sarafianos Sungjoo Yoo Alexander Sorkine-Hornung Rakesh Ranjan Seoul National University Meta Reality Labs

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Shape and geometric patterns are essential in defining stylistic identity. However, current 3D style transfer methods predominantly focus on transferring colors and textures, often overlooking geometric aspects. In this paper, we introduce Geometry Transfer, a novel method that leverages geometric deformation for 3D style transfer. This technique employs depth maps to extract a style guide, subsequently applied to stylize the geometry of radiance fields. Moreover, we propose new techniques that utilize geometric cues from the 3D scene, thereby enhancing aesthetic expressiveness and more accurately reflecting intended styles. Our extensive experiments show that Geometry Transfer enables a broader and more expressive range of stylizations, thereby significantly expanding the scope of 3D style transfer.

关键词： Geometry computer vision Three-dimensional displays Shape Deformation Buildings Color

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Improving Multiple Object Tracking with Single Object Tracking

Improving Multiple Object Tracking with Single Object Tracki...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zheng, Linyu Tang, Ming Chen, Yingying Zhu, Guibo Wang, Jinqiao Lu, Hanqing Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China ObjectEye Inc Beijing Peoples R China

ISBN: (纸本)9781665445092

Despite considerable similarities between multiple object tracking (MOT) and single object tracking (SOT) tasks, modern MOT methods have not benefited from the development of SOT ones to achieve satisfactory performance. The major reason for this situation is that it is inappropriate and inefficient to apply multiple SOT models directly to the MOT task, although advanced SOT methods are of the strong discriminative power and can run at fast speeds. In this paper, we propose a novel and end-to-end trainable MOT architecture that extends CenterNet by adding an SOT branch for tracking objects in parallel with the existing branch for object detection, allowing the MOT task to benefit from the strong discriminative power of SOT methods in an effective and efficient way. Unlike most existing SOT methods which learn to distinguish the target object from its local backgrounds, the added SOT branch trains a separate SOT model per target online to distinguish the target from its surrounding targets, assigning SOT models the novel discrimination. Moreover, similar to the detection branch, the SOT branch treats objects as points, making its online learning efficient even if multiple targets are processed simultaneously. Without tricks, the proposed tracker achieves MOTAs of 0.710 and 0.686, IDF1s of 0.719 and 0.714, on MOT17 and MOT20 benchmarks, respectively, while running at 16 FPS on MOT17.

关键词： computer vision Target tracking Object detection Detectors Benchmark testing Feature extraction pattern recognition

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Rethinking Inductive Biases for Surface Normal Estimation

Rethinking Inductive Biases for Surface Normal Estimation

引用

conference on computer vision and pattern recognition (cvpr)

作者： Gwangbin Bae Andrew J. Davison Dyson Robotics Lab Imperial College London

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Despite the growing demand for accurate surface nor- mal estimation models, existing methods use general- purpose dense prediction models, adopting the same induc- tive biases as other tasks. In this paper, we discuss the inductive biases needed for surface normal estimation and propose to (1) utilize the per-pixel ray direction and (2) en- code the relationship between neighboring surface normals by learning their relative rotation. The proposed method can generate crisp - yet, piecewise smooth - predictions for challenging in-the-wild images of arbitrary resolution and aspect ratio. Compared to a recent ViT-based state- of-the-art model, our method shows a stronger generalization ability, despite being trained on an orders of magni- tude smaller dataset. The code is available at https://***/baegwangbin/DSINE.

关键词： Convolutional codes computer vision Three-dimensional displays Image resolution Image coding Computational modeling Estimation

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ElasticDiffusion: Training-Free Arbitrary Size Image Generation Through Global-Local Content Separation

ElasticDiffusion: Training-Free Arbitrary Size Image Generat...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Moayed Haji-Ali Guha Balakrishnan Vicente Ordonez Rice University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Diffusion models have revolutionized image generation in recent years, yet they are still limited to a few sizes and aspect ratios. We propose ElasticDiffusion, a novel training-free decoding method that enables pretrained text-to-image diffusion models to generate images with various sizes. ElasticDiffusion attempts to decouple the generation trajectory of a pretrained model into local and global signals. The local signal controls low-level pixel information and can be estimated on local patches, while the global signal is used to maintain overall structural consistency and is estimated with a reference image. We test our method on CelebA-HQ (faces) and LAION-COCO (objects/indoor/outdoor scenes). Our experiments and qualitative results show superior image coherence quality across aspect ratios compared to MultiDiffusion and the standard decoding strategy of Stable Diffusion. Project Webpage: https://***/

关键词： computer vision Image synthesis Text to image Coherence Diffusion models Decoding Trajectory

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Bi-Causal: Group Activity recognition via Bidirectional Causality

Bi-Causal: Group Activity Recognition via Bidirectional Caus...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Youliang Zhang Wenxuan Liu Danni Xu Zhuo Zhou Zheng Wang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University of Technology National University of Singapore

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Current approaches in Group Activity recognition (GAR) predominantly emphasize Human Relations (HRs) while often neglecting the impact of Human-Object Inter-actions (HOIs). This study prioritizes the consideration of both HRs and HOIs, emphasizing their interdependence. Notably, employing Granger Causality Tests reveals the presence of bidirectional causality between HRs and HOIs. Leveraging this insight, we propose a Bidirectional-Causal GAR network. This network establishes a causality commu-nication channel while modeling relations and interactions, enabling reciprocal enhancement between human-object interactions and human relations, ensuring their mutual consistency. Additionally, an Interaction Module is devised to effectively capture the dynamic nature of human-object interactions. Comprehensive experiments conducted on two publicly available datasets showcase the superiority of our proposed method over state-of-the-art approaches. Our project page: https://***/***/

关键词： computer vision Computational modeling Cause effect analysis Communication channels Activity recognition

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Deep Spherical Manifold Gaussian Kernel for Unsupervised Domain Adaptation

Deep Spherical Manifold Gaussian Kernel for Unsupervised Dom...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Youshan Davison, Brian D. Lehigh Univ Comp Sci & Engn Bethlehem PA 18015 USA

ISBN: (纸本)9781665448994

Unsupervised Domain adaptation is an effective method in addressing the domain shift issue when transferring knowledge from an existing richly labeled domain to a new domain. Existing manifold-based methods either are based on traditional models or largely rely on Grassmannian manifold via minimizing differences of single covariance matrices of two domains. In addition, existing pseudo-labeling algorithms inadequately consider the quality of pseudo labels in aligning the conditional distribution between two domains. In this work, a deep spherical manifold Gaussian kernel (DSGK) framework is proposed to map the source and target subspaces into a spherical manifold and reduce the discrepancy between them by embedding both extracted features and a Gaussian kernel. To align the conditional distributions, we further develop an easy-to-hard pseudo label refinement process to improve the quality of the pseudo labels and then reduce categorical spherical manifold Gaussian kernel geodesic loss. Extensive experimental results show that DSGK outperforms state-of-the-art methods, especially on challenging cross-domain learning tasks.

关键词： Manifolds Adaptation models computer vision conferences Feature extraction pattern recognition Noise measurement

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Efficient Two-stream Action recognition on FPGA

Efficient Two-stream Action Recognition on FPGA

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Lin, Jia-Ming Lai, Kuan-Ting Wu, Bin-Ray Chen, Ming-Syan Natl Taiwan Univ Taipei Taiwan Natl Taipei Univ Technol Taipei Taiwan

ISBN: (纸本)9781665448994

Action recognition is an important research field that has many applications in surveillance, video search, autonomous vehicles, etc. However, current state-of-the-art action classifiers are still not widely adopted in embedded applications yet. The major reason is that action recognition needs to process both spatial and temporal streaming data to precisely identify actions, which is compute-intensive and power hungry. To solve this issue, researchers start using FPGA to run action recognition models with minimum power. In this paper, we propose a new hardware architecture of action recognition on FPGA. Our model is based on the popular two-stream neural network. By optimizing the optical flow and convolution operations in the temporal domain, our method can achieve similar accuracy with one order of magnitude less operations than other C3D baseline models. We have implemented our model on Xilinx Ultrascale+ ZCU102 and released the source code.

关键词： Convolution Computational modeling Surveillance conferences Neural networks Streaming media Hardware

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